---
title: "CEO Departures from S&P 1500 Firms, 1980-2021"
author: "Antony Rono"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)

library(flexdashboard)
library(tidyverse)
library(tidytuesdayR)
library(janitor)
library(magrittr)
library(plotly)
library(readxl)
library(ggridges)
library(extrafont)
library(scales)

theme_set(theme_light())

options(scipen = 99)
```

```{r Loading Data, include=FALSE}

#tt <- tt_load("2021-04-27")

departures <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-27/departures.csv')

dep_code <- read_xlsx("CEO Departure Code.xlsx")  ## Departure Codes and Title

departures %<>% 
  
  mutate(dismissal_type = case_when(
    
    ceo_dismissal == 0 ~ "Involuntary",
    
    ceo_dismissal == 1 ~ "Voluntary",
    
    TRUE ~ "Uknown"
    
  ),
  
  departure_reason = dep_code$Title[match(departure_code, dep_code$Code)],
  
  departure_reason = ifelse(is.na(departure_reason) | str_detect(departure_reason,"Execucomp error") | str_detect(departure_reason,"Missing") , "Uknown", departure_reason))


```

Overall {data-icon="fa-chart-line"}
=======================================================================

Row {data-height=200}
-----------------------------------------------------------------------

### Number of Companies
```{r number of companies}

valueBox(value = comma(departures %>%  distinct(coname) %>% nrow), caption = "Number of Companies", icon = "fa-industry", color = "green")

```

### Number of Dismissals
```{r number of dismissals}

valueBox(value = comma(departures %>%  nrow), caption = "Number of Dismissals", icon = "fa-times", color = "red")

```

### Breakdown of the Type of Dismissal

```{r Type of Dismissal Overall}

departures %>% 
  
  count(dismissal_type) %>% 
  
  plot_ly(labels = ~ dismissal_type, values = ~ n) %>% 
  
  add_pie(hole = 0.6)


```

Row {data-height=400}
-----------------------------------------------------------------------

### Overall Breakdown by Reason for Departure

```{r Breakdown of all departures Overall}

p <- departures %>% 
  
  count(departure_reason) %>% 
  
  mutate(departure_reason = str_wrap(departure_reason, width = 14)) %>% 
  
  filter(!is.na(departure_reason)) %>% 
  
  mutate(departure_reason = fct_reorder(departure_reason, n, .desc = TRUE)) %>% 
  
  ggplot(aes(departure_reason, n, fill = departure_reason)) +

  geom_col()+

  theme(legend.position =  "none") +


    labs(x = "Reason for Dismissal",
         
         y = "Count"

         )


ggplotly(p, tooltip = c("x", "y"))

```

Row {data-height=400}
-----------------------------------------------------------------------

### Yearly Trend of the Number and Type of Dismissal

```{r Yearly Trend on the Number and Type of Dismissal}

p <- departures %>% 
    
  count(fyear, dismissal_type) %>% 
    
  ggplot(aes(fyear, n, color = dismissal_type))+
    
  geom_point() +
    
  geom_line() +
    
  scale_x_continuous(expand = c(0,0), n.breaks = 10) +
  
  labs(x = "Financial Year",
       
       y = "Number of Dismissal") +
  
  theme(legend.title = element_blank())

ggplotly(p)


```

### Yearly Trend of the Number and Reason for Dismissal

```{r Yearly Trend of the Number and Reason for Dismissal}

p <- departures %>% 
    
  count(fyear, departure_reason) %>% 
    
  ggplot(aes(fyear, n, color = departure_reason))+
    
  geom_point() +
    
  geom_line() +
    
  scale_x_continuous(expand = c(0,0), n.breaks = 10) +
  
  labs(x = "Financial Year",
       
       y = "Reason for Departure") +
  
  theme(legend.title = element_blank()) 

ggplotly(p)


```



Companies and CEOs{data-orientation=columns data-icon="fa-location-arrow"}
=======================================================================

Column
-----------------------------------------------------------------------

### Companies with the most CEO Change - Breakdown by Type of Dismissal

```{r Companies With Most CEOs Switch - Breakdown by Type of Dismissal}

p <- departures %>% 
  
  distinct(coname, exec_fullname, .keep_all = TRUE) %>% 
  
  count(coname, dismissal_type, sort = TRUE) %>% 
  
  ungroup() %>% 
  
  group_by(coname) %>% 
  
  mutate(Total = sum(n)) %>% 
  
  ungroup() %>% 
  
  mutate(coname = fct_reorder(coname, Total, sum)) %>% 
  
  slice_max(Total, n=40) %>% 
  
  ggplot(aes(n, coname,fill = dismissal_type))+
  
  geom_col() +
  
  scale_x_continuous(expand = c(0,0)) +
  
  labs(y = "Company Name",
       
       x = "Count")


ggplotly(p, tooltip = c("y", "fill", "x"))

```


### Companies with the most CEO Change - Breakdown by Reason for Departure

```{r Companies with the most CEO Change - Breakdown by Reason for Departure}

p <- departures %>% 
  
  distinct(coname, exec_fullname, .keep_all = TRUE) %>% 
  
  count(coname,departure_reason, sort = TRUE) %>% 
  
  ungroup() %>% 
  
  group_by(coname) %>% 
  
  mutate(Total = sum(n)) %>% 
  
  ungroup() %>% 
  
  mutate(coname = fct_reorder(coname, Total, sum)) %>% 
  
  slice_max(Total, n=40) %>% 
  
  ggplot(aes(n, coname,fill = departure_reason))+
  
  geom_col() +
  
  scale_x_continuous(expand = c(0,0)) +
  
  labs(y = "Company Name",
       
       x = "Count")

ggplotly(p, tooltip = c("y", "fill", "x"))

```


Column{.tabset}
-----------------------------------------------------------------------

### CEOs with the most dismissals - Breakdown by Type of Dismissal

```{r CEOs with the most dismissal - breakdown by type of dismissal}

p <- departures %>% 
  
  distinct(coname, exec_fullname, .keep_all = TRUE) %>% 
  
  count(exec_fullname, dismissal_type, sort = TRUE) %>% 
  
  ungroup() %>% 
  
  group_by(exec_fullname) %>% 
  
  mutate(Total = sum(n)) %>% 
  
  ungroup() %>% 
  
    arrange(desc(Total)) %>% 
  
  mutate(exec_fullname = fct_reorder(exec_fullname, Total, .desc = FALSE)) %>% 
  
  slice_max(Total, n=5) %>% 
  
  ggplot(aes(n, exec_fullname,fill = dismissal_type))+
  
  geom_col() +
  
  scale_x_continuous(expand = c(0,0)) +
  
  labs(y = "CEO Name",
       
       x = "Count") +
  
  theme(legend.title = element_blank())


ggplotly(p, tooltip = c("y", "fill", "x"))


```

### CEOs with the most dismissals - Breakdown by Reason for Departure

```{r CEOs with the most dismissal - breakdown by reason for departure}

p <- departures %>% 
  
  distinct(coname, exec_fullname, .keep_all = TRUE) %>% 
  
  count(exec_fullname, departure_reason, sort = TRUE) %>% 
  
  ungroup() %>% 
  
  group_by(exec_fullname) %>% 
  
  mutate(Total = sum(n)) %>% 
  
  ungroup() %>% 
  
    arrange(desc(Total)) %>% 
  
  mutate(exec_fullname = fct_reorder(exec_fullname, Total, .desc = FALSE)) %>% 
  
  slice_max(Total, n=15) %>% 
  
  ggplot(aes(n, exec_fullname,fill = departure_reason))+
  
  geom_col() +
  
  scale_x_continuous(expand = c(0,0)) +
  
  labs(y = "CEO Name",
       
       x = "Count") +
  
  theme(legend.title = element_blank())


ggplotly(p, tooltip = c("y", "fill", "x"))

```